Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.979704e-16.
I have a single experimental session, utilising 4 cognitive tasks. I've segmented the original data file into 4 separate runs (by tasks) to make analysis easier (I am using block average not GLM). I seem to be fine with all but one of the runs. The first run is a baseline task composed of a single trial (well a block actually). There is an event several seconds in to the run and all I'm aiming to do is to obtain the HRF during this rest period. I understand I'm essentially not averaging anything (as there is just one stimulus or block in this case). I remember someone mentioning this scaling problem with reference to GLM, but I thought if you are using a block average 1 condition is OK?
Any help would be appreciate
This warning happens with GLM not block average. Please share a screen shot of your processing options. You must be doing a GLM
I really don't see how it can come from recursive tPCA. It must be the GLM function. Share a screen shot of the processing functions. And maybe share your cfg file for the processing options.
Thanks for sharing the full warning message. The full message tells us which function is causing the warning. I should have asked for this in the first place. The message is generated by the filtfilt() function called in the hmrR_bandpassFilt() function. I haven't seen that before.
You set your bandpass filter to 0.01 to 0.1 Hz. I suggest changing 0.1 to 0.5 or even 3 Hz. That might stabilize the bandpass filter.
1) Multiplying by 1e6 would give you uM units
2) It is atypical that a motion correction method would create a motion artifact. Would be good to see a concrete example along with the cfg file.
3) Hb output amplitude can vary depending on whether you did a pathlength correction or not (uncorrected values are for instance 180 larger than corrected ones for a 30 mm channel) or whether there is regression in GLM (short separation regression will typically lower the values).